Knowledge sharing is often built among a group of people within a particular department or sub-organization of an enterprise such as a business or institute. Knowledge beyond a user's own department or sub-organization is hard to acquire unless another user explicitly shares it. Sharing user's knowledge implicitly has great value, for example by presenting users with information regarding “top items of interest”, however such sharing can easily result in information overload because it does not take into account user's specific interests, and may be limited to a user's own social “silo” or explicit community.
Users have become familiar with personalized recommendation systems, for example when shopping on internet shopping sites they may be presented with purchase suggestions (“Shoppers who purchased this item also purchased these items . . . ”), and implementing similar personalized recommendation systems into enterprise networks may have similar advantages of allowing a user to take advantage of the knowledge or preferences of other users who are similarly situated or who have similar interests. However, because the enterprise categorizes users into communities based on factors such as physical location, job title, and the like, existing recommendation systems do not discover or share knowledge with users in an optimal fashion that is customized to a user's interests.